1
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Liu Z, Gillis TG, Raman S, Cui Q. A parameterized two-domain thermodynamic model explains diverse mutational effects on protein allostery. eLife 2024; 12:RP92262. [PMID: 38836839 PMCID: PMC11152574 DOI: 10.7554/elife.92262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2024] Open
Abstract
New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multi-domain allosteric proteins.
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Affiliation(s)
- Zhuang Liu
- Department of Physics, Boston UniversityBostonUnited States
| | - Thomas G Gillis
- Department of Biochemistry, University of WisconsinMadisonUnited States
| | - Srivatsan Raman
- Department of Biochemistry, University of WisconsinMadisonUnited States
- Department of Chemistry, University of WisconsinMadisonUnited States
- Department of Bacteriology, University of WisconsinMadisonUnited States
| | - Qiang Cui
- Department of Physics, Boston UniversityBostonUnited States
- Department of Chemistry, Boston UniversityBostonUnited States
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2
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Simon JJ, Fowler DM, Maly DJ. Multiplexed, multimodal profiling of the intracellular activity, interactions, and druggability of protein variants using LABEL-seq. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.19.590094. [PMID: 38659825 PMCID: PMC11042325 DOI: 10.1101/2024.04.19.590094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Multiplexed assays of variant effect are powerful tools for assessing the impact of protein sequence variation, but are limited to measuring a single protein property and often rely on indirect readouts of intracellular protein function. Here, we developed LAbeling with Barcodes and Enrichment for biochemicaL analysis by sequencing (LABEL-seq), a platform for the multimodal profiling of thousands of protein variants in cultured human cells. Multimodal measurement of ~20,000 variant effects for ~1,600 BRaf variants using LABEL-seq revealed that variation at positions that are frequently mutated in cancer had minimal effects on folding and intracellular abundance but could dramatically alter activity, protein-protein interactions, and druggability. Integrative analysis of our multimodal measurements identified networks of positions with similar roles in regulating BRaf's signaling properties and enabled predictive modeling of variant effects on complex processes such as cell proliferation and small molecule-promoted degradation. LABEL-seq provides a scalable approach for the direct measurement of multiple biochemical effects of protein variants in their native cellular context, yielding insight into protein function, disease mechanisms, and druggability.
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Affiliation(s)
- Jessica J Simon
- Department of Chemistry, University of Washington, Seattle, WA, United States
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA, United States
- Department of Bioengineering, University of Washington, Seattle, WA, United States
- Co-corresponding authors: ,
| | - Dustin J Maly
- Department of Chemistry, University of Washington, Seattle, WA, United States
- Department of Biochemistry, University of Washington, Seattle, WA, United States
- Co-corresponding authors: ,
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3
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Gersing S, Schulze TK, Cagiada M, Stein A, Roth FP, Lindorff-Larsen K, Hartmann-Petersen R. Characterizing glucokinase variant mechanisms using a multiplexed abundance assay. Genome Biol 2024; 25:98. [PMID: 38627865 PMCID: PMC11021015 DOI: 10.1186/s13059-024-03238-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 04/04/2024] [Indexed: 04/19/2024] Open
Abstract
BACKGROUND Amino acid substitutions can perturb protein activity in multiple ways. Understanding their mechanistic basis may pinpoint how residues contribute to protein function. Here, we characterize the mechanisms underlying variant effects in human glucokinase (GCK) variants, building on our previous comprehensive study on GCK variant activity. RESULTS Using a yeast growth-based assay, we score the abundance of 95% of GCK missense and nonsense variants. When combining the abundance scores with our previously determined activity scores, we find that 43% of hypoactive variants also decrease cellular protein abundance. The low-abundance variants are enriched in the large domain, while residues in the small domain are tolerant to mutations with respect to abundance. Instead, many variants in the small domain perturb GCK conformational dynamics which are essential for appropriate activity. CONCLUSIONS In this study, we identify residues important for GCK metabolic stability and conformational dynamics. These residues could be targeted to modulate GCK activity, and thereby affect glucose homeostasis.
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Affiliation(s)
- Sarah Gersing
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark.
| | - Thea K Schulze
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark
| | - Matteo Cagiada
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark
| | - Amelie Stein
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, M5S 3E1, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, M5S 1A8, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, M5G 1X5, Toronto, ON, Canada
- Department of Computational and Systems Biology, University of Pittsburgh School of Medicine, 15213, Pittsburgh, USA
| | - Kresten Lindorff-Larsen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark.
| | - Rasmus Hartmann-Petersen
- The Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Ole Maaløes Vej 5, DK-2200, Copenhagen, Denmark.
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4
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Liu Z, Gillis T, Raman S, Cui Q. A parametrized two-domain thermodynamic model explains diverse mutational effects on protein allostery. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.08.06.552196. [PMID: 37662419 PMCID: PMC10473640 DOI: 10.1101/2023.08.06.552196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
New experimental findings continue to challenge our understanding of protein allostery. Recent deep mutational scanning study showed that allosteric hotspots in the tetracycline repressor (TetR) and its homologous transcriptional factors are broadly distributed rather than spanning well-defined structural pathways as often assumed. Moreover, hotspot mutation-induced allostery loss was rescued by distributed additional mutations in a degenerate fashion. Here, we develop a two-domain thermodynamic model for TetR, which readily rationalizes these intriguing observations. The model accurately captures the in vivo activities of various mutants with changes in physically transparent parameters, allowing the data-based quantification of mutational effects using statistical inference. Our analysis reveals the intrinsic connection of intra- and inter-domain properties for allosteric regulation and illustrate epistatic interactions that are consistent with structural features of the protein. The insights gained from this study into the nature of two-domain allostery are expected to have broader implications for other multidomain allosteric proteins.
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Affiliation(s)
- Zhuang Liu
- Department of Physics, Boston University, Boston, United States
| | - Thomas Gillis
- Department of Biochemistry, University of Wisconsin, Madison, United States
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin, Madison, United States
- Department of Chemistry, University of Wisconsin, Madison, United States
- Department of Bacteriology, University of Wisconsin, Madison, United States
| | - Qiang Cui
- Department of Physics, Boston University, Boston, United States
- Department of Chemistry, Boston University, Boston, United States
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5
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Weng C, Faure AJ, Escobedo A, Lehner B. The energetic and allosteric landscape for KRAS inhibition. Nature 2024; 626:643-652. [PMID: 38109937 PMCID: PMC10866706 DOI: 10.1038/s41586-023-06954-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 12/07/2023] [Indexed: 12/20/2023]
Abstract
Thousands of proteins have been validated genetically as therapeutic targets for human diseases1. However, very few have been successfully targeted, and many are considered 'undruggable'. This is particularly true for proteins that function via protein-protein interactions-direct inhibition of binding interfaces is difficult and requires the identification of allosteric sites. However, most proteins have no known allosteric sites, and a comprehensive allosteric map does not exist for any protein. Here we address this shortcoming by charting multiple global atlases of inhibitory allosteric communication in KRAS. We quantified the effects of more than 26,000 mutations on the folding of KRAS and its binding to six interaction partners. Genetic interactions in double mutants enabled us to perform biophysical measurements at scale, inferring more than 22,000 causal free energy changes. These energy landscapes quantify how mutations tune the binding specificity of a signalling protein and map the inhibitory allosteric sites for an important therapeutic target. Allosteric propagation is particularly effective across the central β-sheet of KRAS, and multiple surface pockets are genetically validated as allosterically active, including a distal pocket in the C-terminal lobe of the protein. Allosteric mutations typically inhibit binding to all tested effectors, but they can also change the binding specificity, revealing the regulatory, evolutionary and therapeutic potential to tune pathway activation. Using the approach described here, it should be possible to rapidly and comprehensively identify allosteric target sites in many proteins.
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Affiliation(s)
- Chenchun Weng
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Andre J Faure
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Albert Escobedo
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Ben Lehner
- Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain.
- University Pompeu Fabra (UPF), Barcelona, Spain.
- Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain.
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
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6
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Xie X, Sun X, Wang Y, Lehner B, Li X. Dominance vs epistasis: the biophysical origins and plasticity of genetic interactions within and between alleles. Nat Commun 2023; 14:5551. [PMID: 37689712 PMCID: PMC10492795 DOI: 10.1038/s41467-023-41188-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 08/25/2023] [Indexed: 09/11/2023] Open
Abstract
An important challenge in genetics, evolution and biotechnology is to understand and predict how mutations combine to alter phenotypes, including molecular activities, fitness and disease. In diploids, mutations in a gene can combine on the same chromosome or on different chromosomes as a "heteroallelic combination". However, a direct comparison of the extent, sign, and stability of the genetic interactions between variants within and between alleles is lacking. Here we use thermodynamic models of protein folding and ligand-binding to show that interactions between mutations within and between alleles are expected in even very simple biophysical systems. Protein folding alone generates within-allele interactions and a single molecular interaction is sufficient to cause between-allele interactions and dominance. These interactions change differently, quantitatively and qualitatively as a system becomes more complex. Altering the concentration of a ligand can, for example, switch alleles from dominant to recessive. Our results show that intra-molecular epistasis and dominance should be widely expected in even the simplest biological systems but also reinforce the view that they are plastic system properties and so a formidable challenge to predict. Accurate prediction of both intra-molecular epistasis and dominance will require either detailed mechanistic understanding and experimental parameterization or brute-force measurement and learning.
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Affiliation(s)
- Xuan Xie
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
| | - Xia Sun
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Yuheng Wang
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK
| | - Ben Lehner
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Dr. Aiguader 88, Barcelona, 08003, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, 08003, Spain.
- ICREA, Pg. Luis Companys 23, Barcelona, 08010, Spain.
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
| | - Xianghua Li
- Zhejiang University - University of Edinburgh Institute, Zhejiang University School of Medicine, Haining, 314400, P. R. China.
- Wellcome Sanger Institute, Wellcome Genome Campus Hinxton, Cambridge, CB10 1SA, UK.
- Deanery of Biomedical Sciences, College of Medicine & Veterinary Medicine, University of Edinburgh, Edinburgh, EH8 9XD, UK.
- Biomedical and Health Translational Centre of Zhejiang Province, Haizhou East Road 718, Haining, 314400, P. R. China.
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7
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Wei H, Li X. Deep mutational scanning: A versatile tool in systematically mapping genotypes to phenotypes. Front Genet 2023; 14:1087267. [PMID: 36713072 PMCID: PMC9878224 DOI: 10.3389/fgene.2023.1087267] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/02/2023] [Indexed: 01/13/2023] Open
Abstract
Unveiling how genetic variations lead to phenotypic variations is one of the key questions in evolutionary biology, genetics, and biomedical research. Deep mutational scanning (DMS) technology has allowed the mapping of tens of thousands of genetic variations to phenotypic variations efficiently and economically. Since its first systematic introduction about a decade ago, we have witnessed the use of deep mutational scanning in many research areas leading to scientific breakthroughs. Also, the methods in each step of deep mutational scanning have become much more versatile thanks to the oligo-synthesizing technology, high-throughput phenotyping methods and deep sequencing technology. However, each specific possible step of deep mutational scanning has its pros and cons, and some limitations still await further technological development. Here, we discuss recent scientific accomplishments achieved through the deep mutational scanning and describe widely used methods in each step of deep mutational scanning. We also compare these different methods and analyze their advantages and disadvantages, providing insight into how to design a deep mutational scanning study that best suits the aims of the readers' projects.
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Affiliation(s)
- Huijin Wei
- Zhejiang University—University of Edinburgh Institute, Zhejiang University, Haining, Zhejiang, China
| | - Xianghua Li
- Zhejiang University—University of Edinburgh Institute, Zhejiang University, Haining, Zhejiang, China,Deanery of Biomedical Sciences, University of Edinburgh, Edinburgh, United Kingdom,The Second Affiliated Hospital of Zhejiang University, Hangzhou, Zhejiang, China,Biomedical and Health Translational Centre of Zhejiang Province, Haining, Zhejiang, China,*Correspondence: Xianghua Li,
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8
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Klinkhammer H, Staerk C, Maj C, Krawitz PM, Mayr A. A statistical boosting framework for polygenic risk scores based on large-scale genotype data. Front Genet 2023; 13:1076440. [PMID: 36704342 PMCID: PMC9871367 DOI: 10.3389/fgene.2022.1076440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 12/20/2022] [Indexed: 01/12/2023] Open
Abstract
Polygenic risk scores (PRS) evaluate the individual genetic liability to a certain trait and are expected to play an increasingly important role in clinical risk stratification. Most often, PRS are estimated based on summary statistics of univariate effects derived from genome-wide association studies. To improve the predictive performance of PRS, it is desirable to fit multivariable models directly on the genetic data. Due to the large and high-dimensional data, a direct application of existing methods is often not feasible and new efficient algorithms are required to overcome the computational burden regarding efficiency and memory demands. We develop an adapted component-wise L 2-boosting algorithm to fit genotype data from large cohort studies to continuous outcomes using linear base-learners for the genetic variants. Similar to the snpnet approach implementing lasso regression, the proposed snpboost approach iteratively works on smaller batches of variants. By restricting the set of possible base-learners in each boosting step to variants most correlated with the residuals from previous iterations, the computational efficiency can be substantially increased without losing prediction accuracy. Furthermore, for large-scale data based on various traits from the UK Biobank we show that our method yields competitive prediction accuracy and computational efficiency compared to the snpnet approach and further commonly used methods. Due to the modular structure of boosting, our framework can be further extended to construct PRS for different outcome data and effect types-we illustrate this for the prediction of binary traits.
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Affiliation(s)
- Hannah Klinkhammer
- Institute for Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University of Bonn, Bonn, Germany
| | - Christian Staerk
- Institute for Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Carlo Maj
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University of Bonn, Bonn, Germany
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Peter Michael Krawitz
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University of Bonn, Bonn, Germany
| | - Andreas Mayr
- Institute for Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
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9
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Tack DS, Tonner PD, Pressman A, Olson ND, Levy SF, Romantseva EF, Alperovich N, Vasilyeva O, Ross D. Precision engineering of biological function with large-scale measurements and machine learning. PLoS One 2023; 18:e0283548. [PMID: 36989327 PMCID: PMC10057847 DOI: 10.1371/journal.pone.0283548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 03/11/2023] [Indexed: 03/30/2023] Open
Abstract
As synthetic biology expands and accelerates into real-world applications, methods for quantitatively and precisely engineering biological function become increasingly relevant. This is particularly true for applications that require programmed sensing to dynamically regulate gene expression in response to stimuli. However, few methods have been described that can engineer biological sensing with any level of quantitative precision. Here, we present two complementary methods for precision engineering of genetic sensors: in silico selection and machine-learning-enabled forward engineering. Both methods use a large-scale genotype-phenotype dataset to identify DNA sequences that encode sensors with quantitatively specified dose response. First, we show that in silico selection can be used to engineer sensors with a wide range of dose-response curves. To demonstrate in silico selection for precise, multi-objective engineering, we simultaneously tune a genetic sensor's sensitivity (EC50) and saturating output to meet quantitative specifications. In addition, we engineer sensors with inverted dose-response and specified EC50. Second, we demonstrate a machine-learning-enabled approach to predictively engineer genetic sensors with mutation combinations that are not present in the large-scale dataset. We show that the interpretable machine learning results can be combined with a biophysical model to engineer sensors with improved inverted dose-response curves.
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Affiliation(s)
- Drew S Tack
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Peter D Tonner
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Abe Pressman
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Nathan D Olson
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Sasha F Levy
- SLAC National Accelerator Laboratory, Menlo Park, CA, United States of America
- Joint Initiative for Metrology in Biology, Stanford, CA, United States of America
| | - Eugenia F Romantseva
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Nina Alperovich
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - Olga Vasilyeva
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
| | - David Ross
- National Institute of Standards and Technology, Gaithersburg, MD, United States of America
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10
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Xia Y, Ning Y, Liu M, Che FE. Recoverable PEG-Supported Amino Alcohol Ligand for Copper-Catalyzed Enantio- and syn-Selective Henry Reaction with Nitroethanol: Sustainable and Straightforward Access to Chiral syn-2-Nitro-1,3-Diols. J Catal 2022. [DOI: 10.1016/j.jcat.2022.11.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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11
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Stephan T, Burgess SM, Cheng H, Danko CG, Gill CA, Jarvis ED, Koepfli KP, Koltes JE, Lyons E, Ronald P, Ryder OA, Schriml LM, Soltis P, VandeWoude S, Zhou H, Ostrander EA, Karlsson EK. Darwinian genomics and diversity in the tree of life. Proc Natl Acad Sci U S A 2022; 119:e2115644119. [PMID: 35042807 PMCID: PMC8795533 DOI: 10.1073/pnas.2115644119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Genomics encompasses the entire tree of life, both extinct and extant, and the evolutionary processes that shape this diversity. To date, genomic research has focused on humans, a small number of agricultural species, and established laboratory models. Fewer than 18,000 of ∼2,000,000 eukaryotic species (<1%) have a representative genome sequence in GenBank, and only a fraction of these have ancillary information on genome structure, genetic variation, gene expression, epigenetic modifications, and population diversity. This imbalance reflects a perception that human studies are paramount in disease research. Yet understanding how genomes work, and how genetic variation shapes phenotypes, requires a broad view that embraces the vast diversity of life. We have the technology to collect massive and exquisitely detailed datasets about the world, but expertise is siloed into distinct fields. A new approach, integrating comparative genomics with cell and evolutionary biology, ecology, archaeology, anthropology, and conservation biology, is essential for understanding and protecting ourselves and our world. Here, we describe potential for scientific discovery when comparative genomics works in close collaboration with a broad range of fields as well as the technical, scientific, and social constraints that must be addressed.
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Affiliation(s)
- Taylorlyn Stephan
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Shawn M Burgess
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Hans Cheng
- Avian Disease and Oncology Laboratory, Agricultural Research Service, US Department of Agriculture, East Lansing, MI 48823
| | - Charles G Danko
- Department of Biomedical Sciences, Baker Institute for Animal Health, Cornell University, Ithaca, NY 14850
| | - Clare A Gill
- Department of Animal Science, Texas A&M University, College Station, TX 77843
| | - Erich D Jarvis
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY 10065
- HHMI, Chevy Chase, MD 20815
| | - Klaus-Peter Koepfli
- Smithsonian-Mason School of Conservation, George Mason University, Front Royal, VA 22630
- Smithsonian Conservation Biology Institute, National Zoological Park, Washington, DC 20008
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - Eric Lyons
- School of Plant Sciences, BIO5 Institute, University of Arizona, Tucson, AZ 85721
| | - Pamela Ronald
- Department of Plant Pathology, University of California, Davis, CA 95616
- The Genome Center, University of California, Davis, CA 95616
- The Innovative Genomics Institute, University of California, Berkeley, CA 94720
- Grass Genetics, Joint Bioenergy Institute, Emeryville, CA 94608
| | - Oliver A Ryder
- San Diego Zoo Wildlife Alliance, Escondido, CA 92027
- Department of Evolution, Behavior, and Ecology, University of California San Diego, La Jolla, CA 92093
| | - Lynn M Schriml
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Pamela Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611
| | - Sue VandeWoude
- Department of Micro-, Immuno-, and Pathology, Colorado State University, Fort Collins, CO 80532
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA 95616
| | - Elaine A Ostrander
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Elinor K Karlsson
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01655;
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
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12
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Revealing modifier variations characterizations for elucidating the genetic basis of human phenotypic variations. Hum Genet 2021; 141:1223-1233. [PMID: 34498116 DOI: 10.1007/s00439-021-02362-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 08/30/2021] [Indexed: 12/23/2022]
Abstract
Epistatic interactions complicate the identification of variants involved in phenotypic effect. In-depth knowledge in modifiers and in pathogenic variants would benefit the mechanistic studies on the genetic basis of complex traits. We systematically compared the modifier variants which have evidence of modifier effect with the pathogenic variants from multiple angles. Our study found that genomic loci of modifier variations differ from pathogenic loci in many aspects, such as population genetics statistics, epigenetic features, evolutionary characteristics and functional properties of the variations. Genes containing modifier variation(s) exhibit higher probability of being haploinsufficient and higher probability of recessive disease causation, and they are relatively more important in network communication. Furthermore, we reinforced that co-expression analysis is an effective methodology to predict functional associations between modifier genes and their potential target genes. In many aspects, we detected statistically significant differences between modifier variants/genes and pathogenic variants/genes, and investigated relationships between modifiers and their potential targets. Our results offer some actionable insights that may provide appropriate guidelines to clinical genetics and researchers to elucidate the molecular mechanism underlying the human phenotypic variation.
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13
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Gao L, Chen X, Lyu X, Ji G, Chen Z, Zhu M, Cao X, Li C, Ji A, Cao Z, Lu N. Tracing the ionic evolution during ILG induced phase transformation in strontium cobaltite thin films. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2021; 33:104004. [PMID: 33570048 DOI: 10.1088/1361-648x/abd1b7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Ionic liquid gating (ILG) that drives the ions incorporate into or extract from the crystal lattice, has emerged as a new pathway to design materials. Although many intriguing emergent phenomena, novel physical properties and functionalities have been obtained, the gating mechanism governing the ion and charge transport remains unexplored. Here, by using the model system of brownmillerite SrCoO2.5 and the corresponding electric-field controlled tri-state phase transformation among the pristine SrCoO2.5, hydrogenated HSrCoO2.5 and oxidized perovskite SrCoO3-δ through the dual ion switch, the ionic diffusion and electronic transport processes were carefully investigated. Through controlling gating experiment by design, we find out that the collaborative interaction between charge transport and ion diffusion plays an essential role to prompt the hydrogen or oxygen ions incorporate into the crystal lattice of SrCoO2.5, and therefore leading to formation of new phases. At region closer to the electrode, the electron can shuttle more readily in (out) the material, correspondingly the incorporation of hydrogen (oxygen) ions and phase transformation is largely affiliated. With the compensated charge of electron as well as the reaction front gradually moving away from the electrode, the new phases would be developed successively across the entire thin film. This result unveils the underlying mechanism in the electric-field control of ionic incorporation and extraction, and therefore provides important strategy to achieve high efficient design of material functionalities in complex oxide materials.
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Affiliation(s)
- Lei Gao
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Xiaokun Chen
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Xiangyu Lyu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Guiping Ji
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Zhanfen Chen
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Center for Optoelectronics Materials and Devices & Key Laboratory of Optical Field Manipulation of Zhejiang Province, Department of Physics, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Mingtong Zhu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Xun Cao
- State Key Laboratory of High Performance Ceramics and Superfine Microstructure, Shanghai Institute of Ceramics, Chinese Academy of Sciences, Dingxi Road 1295, Changning, Shanghai 200050, People's Republic of China
| | - Chaorong Li
- Center for Optoelectronics Materials and Devices & Key Laboratory of Optical Field Manipulation of Zhejiang Province, Department of Physics, Zhejiang Sci-Tech University, Hangzhou 310018, People's Republic of China
| | - Ailing Ji
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
| | - Zexian Cao
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, People's Republic of China
| | - Nianpeng Lu
- Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190, People's Republic of China
- Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, People's Republic of China
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